Abstract
One of the ways of acquiring new knowledge or underlying patterns in data is by means of clustering algorithms or techniques for creating groups of objects or individuals with similar characteristics in each group and at the same time different from the other groups. There is a consensus in the scientific community that the most widely used clustering algorithm is K-means, mainly because its results are easy to interpret and there are different implementations. In this paper we present an exploratory analysis of the behavior of the main variants of the K-means algorithm (HartiganWong, Lloyd, Forgy and MacQueen) when solving some of the difficult sets of instances from the Fundamental Clustering Problems Suite (FCPS) benchmark. These variants are implemented in the R language and allow finding the minimum and maximum intra-cluster distance of the final clustering. The different scenarios are shown with the results obtained.
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